Comprehensive Telestroke Network to Optimize Health Care Delivery for Cerebrovascular Diseases: Algorithm Development

Bayona, Hernán; Ropero, Brenda; Salazar, Antonio J.; Pérez, Juan Camilo; Granja, Manuel F; Martínez, Carlos; Useche, Juan Nicolás

doi:10.2196/18058

Cited by 4 publications

(3 citation statements)

References 32 publications

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“…Due to the standardization of initial clinical examination by automated scoring systems and decision-making algorithms, documentation quality can increase up to 100% [26]. The extensive use of telemedicine and teleradiology optimize the consultation and the coordination of the accurate decision for urgently transporting the patient to the endovascular center [28][29][30].…”

Section: Discussionmentioning

confidence: 99%

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How to Improve the Management of Acute Ischemic Stroke by Modern Technologies, Artificial Intelligence, and New Treatment Methods

Zeleňák

Krajina

Meyer

et al. 2021

Life

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Stroke remains one of the leading causes of death and disability in Europe. The European Stroke Action Plan (ESAP) defines four main targets for the years 2018 to 2030. The COVID-19 pandemic forced the use of innovative technologies and created pressure to improve internet networks. Moreover, 5G internet network will be helpful for the transfer and collecting of extremely big databases. Nowadays, the speed of internet connection is a limiting factor for robotic systems, which can be controlled and commanded potentially from various places in the world. Innovative technologies can be implemented for acute stroke patient management soon. Artificial intelligence (AI) and robotics are used increasingly often without the exception of medicine. Their implementation can be achieved in every level of stroke care. In this article, all steps of stroke health care processes are discussed in terms of how to improve them (including prehospital diagnosis, consultation, transfer of the patient, diagnosis, techniques of the treatment as well as rehabilitation and usage of AI). New ethical problems have also been discovered. Everything must be aligned to the concept of “time is brain”.

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Section: Discussionmentioning

confidence: 99%

“…For sites without in-house imaging interpretation expertise, teleradiology systems are recommended [28]. A comprehensive telestroke network and interconnected system of endovascular centers must be established to create an effective health care system [29,30].…”

Section: Team Activation Consultation and Transfermentioning

confidence: 99%

How to Improve the Management of Acute Ischemic Stroke by Modern Technologies, Artificial Intelligence, and New Treatment Methods

Zeleňák

Krajina

Meyer

et al. 2021

Life

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“…Approximately one-third of potentially eligible patients failed to receive alteplase (tissue plasminogen activator), the gold-standard treatment for acute ischemic stroke [ 10 ]. This is because of either the lack of available specialists to perform appropriate clinical assessments or delays in the process of referring to health care facilities with the required stroke-handling capabilities [ 11 ]. In particular, Black people, Hispanic people, women, older people on Medicare, and people in rural areas are more prone to misdiagnosis and delay in receiving tissue plasminogen activator after having a stroke [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach to Support Urgent Stroke Triage Using Administrative Data and Social Determinants of Health at Hospital Presentation: Retrospective Study

Chen¹,

Tan²,

Padman³

2023

J Med Internet Res

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Background The key to effective stroke management is timely diagnosis and triage. Machine learning (ML) methods developed to assist in detecting stroke have focused on interpreting detailed clinical data such as clinical notes and diagnostic imaging results. However, such information may not be readily available when patients are initially triaged, particularly in rural and underserved communities. Objective This study aimed to develop an ML stroke prediction algorithm based on data widely available at the time of patients’ hospital presentations and assess the added value of social determinants of health (SDoH) in stroke prediction. Methods We conducted a retrospective study of the emergency department and hospitalization records from 2012 to 2014 from all the acute care hospitals in the state of Florida, merged with the SDoH data from the American Community Survey. A case-control design was adopted to construct stroke and stroke mimic cohorts. We compared the algorithm performance and feature importance measures of the ML models (ie, gradient boosting machine and random forest) with those of the logistic regression model based on 3 sets of predictors. To provide insights into the prediction and ultimately assist care providers in decision-making, we used TreeSHAP for tree-based ML models to explain the stroke prediction. Results Our analysis included 143,203 hospital visits of unique patients, and it was confirmed based on the principal diagnosis at discharge that 73% (n=104,662) of these patients had a stroke. The approach proposed in this study has high sensitivity and is particularly effective at reducing the misdiagnosis of dangerous stroke chameleons (false-negative rate <4%). ML classifiers consistently outperformed the benchmark logistic regression in all 3 input combinations. We found significant consistency across the models in the features that explain their performance. The most important features are age, the number of chronic conditions on admission, and primary payer (eg, Medicare or private insurance). Although both the individual- and community-level SDoH features helped improve the predictive performance of the models, the inclusion of the individual-level SDoH features led to a much larger improvement (area under the receiver operating characteristic curve increased from 0.694 to 0.823) than the inclusion of the community-level SDoH features (area under the receiver operating characteristic curve increased from 0.823 to 0.829). Conclusions Using data widely available at the time of patients’ hospital presentations, we developed a stroke prediction model with high sensitivity and reasonable specificity. The prediction algorithm uses variables that are routinely collected by providers and payers and might be useful in underresourced hospitals with limited availability of sensitive diagnostic tools or incomplete data-gathering capabilities.

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Automated ASPECTS Segmentation and Scoring Tool: a Method Tailored for a Colombian Telestroke Network

Ortiz,

Rivera,

Granja

et al. 2024

j Imaging. Inform. med.

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To evaluate our two non-machine learning (non-ML)-based algorithmic approaches for detecting early ischemic infarcts on brain CT images of patients with acute ischemic stroke symptoms, tailored to our local population, to be incorporated in our telestroke software. One-hundred and thirteen acute stroke patients, excluding hemorrhagic, subacute, and chronic patients, with accessible brain CT images were divided into calibration and test sets. The gold standard was determined through consensus among three neuroradiologist. Four neuroradiologist independently reported Alberta Stroke Program Early CT Scores (ASPECTSs). ASPECTSs were also obtained using a commercial ML solution (CMLS), and our two methods, namely the Mean Hounsfield Unit (HU) relative difference (RELDIF) and the density distribution equivalence test (DDET), which used statistical analyze the of the HUs of each region and its contralateral side. Automated segmentation was perfect for cortical regions, while minimal adjustment was required for basal ganglia regions. For dichotomized-ASPECTSs (ASPECTS < 6) in the test set, the area under the receiver operating characteristic curve (AUC) was 0.85 for the DDET method, 0.84 for the RELDIF approach, 0.64 for the CMLS, and ranged from 0.71–0.89 for the neuroradiologist. The accuracy was 0.85 for the DDET method, 0.88 for the RELDIF approach, and was ranged from 0.83 − 0.96 for the neuroradiologist. Equivalence at a margin of 5% was documented among the DDET, RELDIF, and gold standard on mean ASPECTSs. Noninferiority tests of the AUC and accuracy of infarct detection revealed similarities between both DDET and RELDIF, and the CMLS, and with at least one neuroradiologist. The alignment of our methods with the evaluations of neuroradiologist and the CMLS indicates the potential of our methods to serve as supportive tools in clinical settings, facilitating prompt and accurate stroke diagnosis, especially in health care settings, such as Colombia, where neuroradiologist are limited.

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Comprehensive Telestroke Network to Optimize Health Care Delivery for Cerebrovascular Diseases: Algorithm Development

Cited by 4 publications

References 32 publications

How to Improve the Management of Acute Ischemic Stroke by Modern Technologies, Artificial Intelligence, and New Treatment Methods

How to Improve the Management of Acute Ischemic Stroke by Modern Technologies, Artificial Intelligence, and New Treatment Methods

A Machine Learning Approach to Support Urgent Stroke Triage Using Administrative Data and Social Determinants of Health at Hospital Presentation: Retrospective Study

Automated ASPECTS Segmentation and Scoring Tool: a Method Tailored for a Colombian Telestroke Network

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